metrics.LinearBuckets(1, 1, 59),      // 1s, 2s, 3s, ... 59s
				metrics.LinearBuckets(60, 5, 12),     // 60s, 65s, 70s, ... 115s
				metrics.LinearBuckets(120, 30, 7),    // 2min, 2.5min, 3min, ..., 5min
			),
			StabilityLevel: metrics.ALPHA,
		},
	)

	// EndpointChangesPending is the number of pending endpoint changes that
	// have not yet been synced to the proxy.

| `minio_node_cpu_avg_load5`           | CPU load average 5min.                     |
| `minio_node_cpu_avg_load5_avg`       | CPU load average 5min (avg).               |
| `minio_node_cpu_avg_load5_max`       | CPU load average 5min (max).               |
| `minio_node_cpu_avg_load5_perc`      | CPU load average 5min (percentage).        |

		cpuSteal:          "CPU steal time",
		cpuNice:           "CPU nice time",
		cpuLoad1:          "CPU load average 1min",
		cpuLoad5:          "CPU load average 5min",
		cpuLoad15:         "CPU load average 15min",
		cpuLoad1Perc:      "CPU load average 1min (perentage)",
		cpuLoad5Perc:      "CPU load average 5min (percentage)",
		cpuLoad15Perc:     "CPU load average 15min (percentage)",
	}
	resourceMetricsGroups = []*MetricsGroupV2{

		// become normal. Check again.
		if e < emin {
			// denormal number: recompute precision
			// Since rounding may have at best increased precision
			// and we have eliminated p <= 0 early, we know p > 0.
			// bexp == 0 for denormals
			p = mbits + 1 - emin + int(e)
			mant = msb32(r.mant) >> uint(fbits-p)
		} else {
			// normal number: emin <= e <= emax
			bexp = uint32(e+bias) << mbits

		// longer lasting concerns which will eventually be retried on 1min interval.
		// Unlike the mutating webhook controller, we do not use NewItemFastSlowRateLimiter. This is because
		// the validation controller waits for its own service to be ready, so typically this takes a few seconds
		// before we are ready; using FastSlow means we tend to always take the Slow time (1min).

               "options": {
                  "calculate": true,
                  "calculation": {
                     "xBuckets": {
                        "mode": "size",
                        "value": "1min"
                     }
                  },
                  "cellGap": 2,
                  "color": {
                     "mode": "scheme",
                     "scheme": "Spectral",

         },
         "id": 13,
         "interval": "1m",
         "options": {
            "calculation": {
               "xBuckets": {
                  "mode": "size",
                  "value": "1min"
               }
            },
            "cellGap": 0,
            "color": {
               "mode": "scheme",
               "scheme": "Spectral",
               "steps": 128
            },

| `minio_system_cpu_avg_iowait` | `gauge` | Average CPU IOWait time            | `server` |
| `minio_system_cpu_load`       | `gauge` | CPU load average 1min              | `server` |
| `minio_system_cpu_load_perc`  | `gauge` | CPU load average 1min (percentage) | `server` |
| `minio_system_cpu_nice`       | `gauge` | CPU nice time                      | `server` |

          self._bias = array_ops.constant(
              np.random.uniform(size=bias_shape), dtype=dtypes.float32
          )

        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(

			return
		}
		if nargs < min || min+1 < nargs {
			check.errorf(call, WrongArgCount, invalidOp+"%v expects %d or %d arguments; found %d", call, min, min+1, nargs)
			return
		}

		types := []Type{T}
		var sizes []int64 // constant integer arguments, if any
		for _, arg := range argList[1:] {